Diraq says quantum computers could cut AI’s energy use for data training

Some new artificial intelligence models are expected to exceed 1 MW, consuming more electricity than an average US home uses in two years, as global data center electricity generation surges toward 1,000 terawatt-hours by 2030. While much attention focuses on optimizing power grids or molecule design, creating high-quality training data for AI, a key driver of this energy demand, is a less discussed application where quantum computing could offer a solution. Diraq argues that quantum computers can fill this training data gap, potentially replacing energy-intensive AI racks with calculations performed with ease, though quantum machines will still work alongside classical systems. The creation of high-quality training data represents a largely overlooked application for quantum computing, potentially addressing a key driver of artificial intelligence’s escalating energy demands. Classical computers struggle with complex…